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The Evolution of Reinforcement Learning in Quantitative Finance
Reinforcement Learning (RL) has experienced significant advancement over the past decade, prompting a growing interest in applications within finance.
From Stem to Stern: Contestability Along AI Value Chains
As an outcome of the workshop, we will synthesize the most pressing opportunities and challenges for contestability along AI value chains in the form of a research roadmap.
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics
Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks.
Progressive Data Science: Potential and Challenges
We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.
